Remote Dwelling Location Is a Risk Factor for CKD Among Indigenous Canadians
Bibliographic record
Abstract
INTRODUCTION: Rural and remote indigenous individuals have a high burden of chronic kidney disease (CKD) when compared to the general population. However, it has not been previously explored how these rates compare to urban-dwelling indigenous populations. METHODS: In a recent cross-sectional screening study, 1346 adults 18 to 80 years of age were screened for CKD and diabetes across 11 communities in rural and remote areas in Manitoba, Canada, as part of the First Nations Community Based Screening to Improve Kidney Health and Prevent Dialysis (FINISHED) program. An additional 284 Indigenous adults who resided in low-income areas in the city of Winnipeg, Manitoba, Canada were screened as part of the NorWest Mobile Diabetes and Kidney Disease Screening and Intervention Project. RESULTS: Our findings indicate that a gradient of CKD and diabetes prevalence exists for Indigenous individuals living in different geographic areas. Compared to urban-dwelling Indigenous individuals, rural-dwelling individuals had more than a 2-fold (2.1, 95% CI = 1.4-3.1) increase in diabetes whereas remote-dwelling individuals had a 4-fold (4.1, 95% CI = 2.8-6.0) increase, and more than a 3-fold (3.1, 95% CI = 2.2-4.5) increase in CKD prevalence. CONCLUSION: Although these results highlight the relative importance of geography in determining the prevalence of diabetes and CKD in Indigenous Canadians, geography is but an important surrogate of other determinants, such as poverty and access to care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".